As an image display device increasingly develops, people have higher requirements for high-quality and high definition image information. In practice, a digital image is usually affected by an imaging device, noise interference in an external environment, and the like, during processes of digitalization and transmission. Therefore, such a digital image with noise interference is usually referred to as an image with noise or a noisy image. Noise may reduce a resolution of a digital image and affects display details of the image, which is extremely disadvantageous to subsequent processing of the image. Therefore, effective noise suppression is essential to an image application. Image noise reduction is of great significance in a video processing system. In a television system, deinterlacing, anti-aliasing, and image scaling require that the system provide an image without noise or with low noise as input image information. In a surveillance system, image noise reduction is also a main method for improving quality of a surveillance image.
TNR is an important technical method for image noise reduction. A TNR method that is commonly used in the prior art can be implemented in the following manner:pixeltnr(x,y,t)=pixeltnr(x,y,t+Δt)×alpha+pixel(x,y,t)×(1−alpha)where pixel indicates an original noisy image, pixeltnr is an image obtained after TNR, and in a digital image, the foregoing variables are both replaced with discrete variables, x and y are two-dimensional space coordinates, and t is a one-dimensional time coordinate, where x and y determine a position of an indicated pixel, and t represents a position of a current image in an image sequence, that is, a quantity of frames, Δt is a time offset, and Δt is usually set to 1, alphaε[0,1], and alpha is a blending coefficient and is used to determine a noise reduction intensity, a larger alpha indicates a higher noise reduction intensity, and vice versa.
The TNR in the prior art is mainly TNR based on determining of movement/still. A movement level of corresponding image content is determined according to a size of a frame difference, and a corresponding blending coefficient is selected according to the movement level. When the frame difference is lower, it is considered that a corresponding movement level is lower, a movement is tending to be still, and a higher blending coefficient alpha is selected, and vice versa.
During a process of implementing the present disclosure, the inventors of the present disclosure found that in the TNR based on determining of movement/still, only a frame difference is used as a basis for determining whether an image moves, and a corresponding blending coefficient is selected according to a result of the determining. However, a detection error may easily occur if only a frame difference is used as a basis for determining a movement of an image. If a moving image is determined to be a still image, smearing of the image may occur, and details of the image may be lost. If a still image is determined to be a moving image, a noise reduction effect may be poor for an image with large noise. Therefore, the TNR based on determining of movement/still cannot adapt to different noise scenarios.